ライフサイエンスコーパス: of the variance in

1 Of the variance in the adequacy of cardiac monitoring, 1

2 gether, these 7 distinct loci explained 6.0% of the variance in TAO.

3 baseline PET data explained 87% (P < 0.001) of the variance in longitudinal accumulation rate across

4 baseline PET data explained 87% (P < 0.001) of the variance in longitudinal accumulation rate across

5 enetic variants accounts for 0.08% (P=0.020) of the variance in BMI and a genetic profile score using

6 5, P = 2.9 x 10(-69)), which explained 17.1% of the variance in adiponectin levels and largely accoun

7 UK Biobank sample captured approximately 1% of the variance in neuroticism in the GS:SFHS and QIMR s

8 high-IF journals, but only approximately 1% of the variance in time-to-retraction was explained by i

9 olymorphism (SNP) explained approximately 1% of the variance in toenail Se concentrations.

10 ned up to 15% of the variance in PCS and 10% of the variance in MCS.

11 ICC estimated that 10% of the variance in surface caries is attributable to the

12 ta sets for instruments explaining up to 10% of the variance in the exposure with sample sizes up to

13 ntly associated with HSV-2 and explained 11% of the variance in prevalence.

14 polygenic risk scores explaining up to 0.12% of the variance in ALS (P=8.4 x 10(-7)).

15 Genotype accounted for 12% of the variance in dopamine release in the nucleus accum

16 These loci accounted for 12% of the variance in MMA concentration.

17 We also showed that 12% of the variance in DD was accounted for by genotype and

18 riant in HBE accounted for an additional 13% of the variance in induced levels, while variants in the

19 Distractibility, however, accounted for 13% of the variance in men's energy intake (P = 0.11).

20 stimated effect size accounting for over 13% of the variance in fear recognition.

21 two loci that account for approximately 14% of the variance in PT were detected and supported by fun

22 Common genetic variants explain 15% of the variance in neuroticism.

23 The 24-h CHO-Ox explained 15% of the variance in %EN-WM.

24 Together, these loci explained 15% of the variance in cortical Abeta levels in this sample

25 owever, patching accounted for less than 15% of the variance in logMAR acuity at 4(1/2) years of age.

26 The genotype at rs4606 explained 10% to 15% of the variance in amygdala and insular cortex activatio

27 c and clinical variables explained up to 15% of the variance in PCS and 10% of the variance in MCS.

28 d healthcare resources explained 11% and 16% of the variance in mortality and readmission rates, beyo

29 social contact between twins contributed 16% of the variance in BMI change (P < 0.001), whereas genet

30 OD > 8.0) and accounts for approximately 17% of the variance in plasma adiponectin levels in a sample

31 nalyses, dietary GI accounted for 10% to 18% of the variance in each glycemic variable, independent o

32 Prefrontal changes explained 19% of the variance in final Hamilton depression scale score

33 Using polygenic prediction analysis, ~1.2% of the variance in general cognitive function was predic

34 pants accounted for up to approximately 1.2% of the variance in outcomes in STAR*D, suggesting a weak

35 e partners contributed only approximately 2% of the variance in early set-point viral loads of seroco

36 This risk score explained 2% of the variance in childhood body mass index.

37 association study (N=127,000), explained 2% of the variance in total years of education (EduYears).

38 f the UF; these effects explained 39 and 20% of the variance in FA values for left and right frontal

39 cerevisiae TRmD represents approximately 20% of the variance in translation and directs an amplificat

40 linergic activity uniquely accounted for 20% of the variance in global cognition change, independent

41 All common variants explain >/=20% of the variance in TSH and FT4.

42 to laminar modulus and position (24% and 21% of the variance in LCD, respectively), whereas SCE was m

43 sting C-peptide and waist circumference, 21% of the variance in PAI-1.

44 ed 0.6% (P = 6.6E-08) and 2.3% (P = 6.9E-21) of the variance in refractive error at ages 7 and 15, re

45 n status, which alone accounted for only 22% of the variance in WM capacity.

46 ty in this MPFC ROI predicted an average 23% of the variance in behavior change beyond the variance p

47 erating only in males and accounting for 23% of the variance in liability.

48 72% accuracy and explained approximately 24% of the variance in adherence.

49 nd-expiratory pressure (p < .0001), only 24% of the variance in PL was explained by Pao (R = .243), a

50 d videokeratoscopy, accounting for up to 24% of the variance in lens movement.

51 significant and explained between 18 and 25% of the variance in the mixing ratio of these carbonyls.

52 cleus accumbens DA release accounted for 25% of the variance in placebo analgesic effects.

53 negative predictor) scales accounted for 25% of the variance in placebo analgesic responses.

54 and neonatal epigenetic marks explained >25% of the variance in childhood adiposity.

55 ta diversity patterns, which explained 7-27% of the variance in TBD and PBDt, whereas the spatial var

56 nted for 46% of the variance in PTSD and 27% of the variance in MDD.

57 a current psychotic episode, explaining 27% of the variance in symptom severity (n = 32, r = 0.52, P

58 depressive behaviors and sex, explained 28% of the variance in follow-up PGBI-10M.

59 dren, the prediction model accounted for 28% of the variance in HRQL and included perceived disease s

60 onse to reward expectation accounted for 28% of the variance in the formation of placebo analgesia.

61 association was found to explain 31 and 29% of the variance in HSPA1B expression following heat shoc

62 rphisms (SNPs) explained between 20% and 29% of the variance in MDD risk, and the heritability in MDD

63 etic loci that account for approximately 29% of the variance in aPTT and two loci that account for ap

64 nsporter gene, SLC2A9, that explain 1.7-5.3% of the variance in serum uric acid concentrations, follo

65 GWAS set optimally explains approximately 3% of the variance in MS risk in our independent target GWA

66 Together the 41 loci explain 3% of the variance in plasma fibrinogen concentration.

67 AC treatments explained 3% of the variance in the community data.

68 attributable to the individual level and 30% of the variance in surfaces caries is attributable to va

69 s estimated to account for approximately 30% of the variance in EM.

70 he FW2.2 gene is hypothesized to control 30% of the variance in fruit weight by negatively regulating

71 neural specificity measure accounted for 30% of the variance in a composite measure of fluid processi

72 the potential correlates, accounted for 30% of the variance in the score for self-rated successful a

73 The audiogram predicts <30% of the variance in speech-reception thresholds (SRTs) fo

74 olling for block effects between 23% and 31% of the variance in the data could be explained by densit

75 tion of the trunk (95% CI: 0%, 44%), and 31% of the variance in the time spent in sedentary behavior

76 ressful life events on severity, explain 31% of the variance in major depression severity.

77 te body consciousness together explained 31% of the variance in self-objectification.

78 these collectively account for at least 32% of the variance in liability.

79 About 33% of the variance in students' performance is predicted by

80 seline fetal hemoglobin levels explained 33% of the variance in induced levels.

81 in MD were owing to genetic factors, and 34% of the variance in PG and 59% of the variance in MD were

82 fined grains, and salty snacks explained 34% of the variance in GI and 68% of the variance in GL.

83 ty physical activity (95% CI: 29%, 54%), 35% of the variance in acceleration of the trunk (95% CI: 0%

84 O, HFEN and HFEN+ explained 34%, 30% and 36% of the variance in daily PAEE, respectively, compared to

85 o scleral modulus and thickness (46% and 36% of the variance in SCE, respectively).

86 OTC pharmaceutical sales explain 36% of the variance in the patient volume, and each standard

87 graph theoretical attributes account for 36% of the variance in biological process enrichment.

88 riation existed, with VA explaining only 36% of the variance in LCA performance for control data.

89 esults from the YSD screen could explain 37% of the variance in the fitness landscapes for one enzyme

90 1C, visceral fat mass, and M:I explained 38% of the variance in Kf (in a linear regression model with

91 explained an additional 3.4%, 4.6%, and 2.4% of the variance in BMI, BMI z scores, and total fat mass

92 ocus on chromosome 2R, which explained 24.4% of the variance in resistance.

93 M sleep consolidation that night, with 28.4% of the variance in increased REM sleep consolidation fro

94 Our conceptual model explained 53.4% of the variance in asthma severity.

95 six factors, which explained 77.8% and 65.4% of the variance in exploratory and constrained explorato

96 multiple regression model showed that 86.4% of the variance in underreporting error was explained by

97 nd is particularly strong, accounting for 4% of the variance in liability to diabetes.

98 ic factors may account for as much as 30-40% of the variance in SWB.

99 Our multiple regression model described 40% of the variance in 25(OH)D concentration; modifiable beh

100 More than 40% of the variance in the B/Y - W/W mean deviation differen

101 linical severity accounted for more than 40% of the variance in treatment response and substantially

102 riate prediction models accounted for 10-41% of the variance in change in %BF.

103 cated that 66% of the variance in PG and 41% of the variance in MD were owing to genetic factors, and

104 olymorphic codon 129 was found to confer 41% of the variance in age of onset but interestingly this p

105 information processing account for up to 42% of the variance in global functional status in schizophr

106 Length of perfect match explained 43% of the variance in log2 signal ratios between probes wit

107 n the quality of the input data explains 43% of the variance in the quality of published de novo tran

108 epressant treatment response, predicting 43% of the variance in symptom improvement at the end of the

109 on the executive composite accounted for 44% of the variance in language composite scores.

110 ge frequencies (explaining approximately 45% of the variance in standard regression models).

111 Six components, explaining 46% of the variance in recorded symptoms, were extracted.

112 Heritable influences accounted for 46% of the variance in PTSD and 27% of the variance in MDD.

113 tisfaction Questionnaire-Short Form) and 47% of the variance in changes in functioning (Work and Soci

114 tic factors (ie, heritability) explained 47% of the variance in physical activity energy expenditure

115 In the best-fitting genetic model, 47% of the variance in low-risk trauma exposure and 60% of t

116 of Cu speciation with WHAM VI explained 49% of the variance in measured Cu activity.

117 vided a good fit to the data, explaining 49% of the variance in the liability to depressive episodes.

118 on in these regions accounted for 17% to 49% of the variance in these personality traits.

119 YrEd) did, explaining up to an additional 5% of the variance (in CC).

120 0.003), and the T risk allele explained 1.5% of the variance in HDL-C levels.

121 tion of fear and safety learning, with 22.5% of the variance in startle retention accounted for by RE

122 OD score 2.9) that explained 19.2% and 24.5% of the variance in VWF levels, respectively.

123 alysis revealed that microbiota explain 4.5% of the variance in body mass index, 6% in triglycerides,

124 Finally, we show that 46%-52.5% of the variance in body size of dog breeds can be explai

125 in translation and explains approximately 5% of the variance in protein expression.

126 mass index (BMI) and obesity account for <5% of the variance in BMI.

127 with visual outcome, accounting for only 5% of the variance in vision between patients, and should p

128 ygenic score analyses indicate that up to 5% of the variance in cognitive test scores can be predicte

129 ity together accounted for approximately 50% of the variance in AER performance across individuals.

130 etic factors accounted for approximately 50% of the variance in compulsive hoarding, with nonshared e

131 he predictive model for FEV(1) explained 50% of the variance in FEV(1), and the model for severe COPD

132 between individual cells but explained >50% of the variance in the population's average protein abun

133 matology-Self Report) accounted for only 50% of the variance in changes in QOL (Quality of Life Enjoy

134 In one case, a single QTL explained over 50% of the variance in the F2, suggesting that at least one

135 More than 50% of the variance in fibrinogen gamma' and gamma'/total fi

136 and cultures, factor analysis shows that 50% of the variance in rating scales is accounted for by jus

137 the minor G allele] and accounted for 0.54% of the variance in serum calcium concentrations.

138 lplessness, but the model predicted only 54% of the variance in mental fatigue scores.

139 ned up to 60% of the variance in PCS and 56% of the variance in MCS; demographic and clinical variabl

140 ts of attention and sequencing explained 56% of the variance in structural speech disorder.

141 alence of ADHD was found, explaining 34%-57% of the variance in ADHD prevalence, with high SI having

142 differences in the larval period explain 57% of the variance in relative limb length and 33% of snout

143 losum and anterior commissure) explained 57% of the variance in language abilities.

144 The model accounted for 57% of the variance in visual acuity and provided a better f

145 on this approach was able to account for 58% of the variance in raters' impressions of previously uns

146 ctors, and 34% of the variance in PG and 59% of the variance in MD were owing to unique environmental

147 opulation level, TBI explained between 2%-6% of the variance in the examined outcomes.

148 7.5% and 5.6% of the variance in compulsory admission occurred at LSOA

149 B4, CLU, and HFE) explained approximately 6% of the variance in the average fractional anisotropy (FA

150 est-fit model run, which explains almost 60% of the variance in global ice volume during the past 400

151 variance in low-risk trauma exposure and 60% of the variance in high-risk trauma exposure was attribu

152 he gene dose of var3, with approximately 60% of the variance in expression accounted for by genotype

153 i of piggyBac elements could account for 60% of the variance in position-dependent activity observed

154 affect circumplex--accounted for nearly 60% of the variance in beauty ratings.

155 nalysis, symptom classes explained up to 60% of the variance in PCS and 56% of the variance in MCS; d

156 ls in the orbitofrontal cortex explained 61% of the variance in a measure of behavioral flexibility b

157 he overall prediction, accounting for 24-62% of the variance in change in %BF in those groups in whic

158 The regression models explained 62% of the variance in FSS and 78% of the variance in ProF-S

159 and diffuse tissue damage accounted for 62% of the variance in GM atrophy in RRMS, but there were no

160 ults, the prediction model accounted for 62% of the variance in HRQL and included perceived disease s

161 a sample of 1240 Hispanic Americans and 63% of the variance in families carrying the mutation.

162 MERGANSER explained 63% of the variance in fish and loon Hg concentrations.

163 notropic response collectively explained 64% of the variance in raw peak oxygen consumption (mL/min).

164 ein cholesterol (HDL-C) levels explained 65% of the variance in the TG:VLDL-C ratio.

165 ted with the degree of VFD and explained 65% of the variance in this measure.

166 FIB removal in biofilters, we find that 66% of the variance in FIB removal rates can be explained by

167 t-fitting bivariate model indicated that 66% of the variance in PG and 41% of the variance in MD were

168 explained 34% of the variance in GI and 68% of the variance in GL.

169 g the 14 lakes considered, and explained 68% of the variance in THg concentration in surface sediment

170 This model accounts for 69% of the variance in mean microcystin concentrations in la

171 alleles in these genes explained 6.1%-14.7% of the variance in the five lipid-related traits, and in

172 f patients and practices explained only 2.7% of the variance in exception reporting.

173 l diameter, ACD, and I-Curv) explained 36.7% of the variance in APAC occurrence, with ACD accounting

174 ctors of IPS and EF and helped explain 52.7% of the variance in IPS and EF.

175 ong interactions between factors (35% and 7% of the variance in LCD and SCE, respectively).

176 erbal learning and memory, accounting for 7% of the variance in these measures, independent of age, I

177 bles of energy metabolism predicted up to 7% of the variance in changes in %BF over the 2-y interval

178 variants accounts for 0.42% (P=1.9 x 10(-7)) of the variance in cognitive function.

179 Overall, the gBGC model explains 70% of the variance in SCU among genes.

180 s between TFs explains a large portion (72%) of the variance in expression of these CREs.

181 We also show that 66-73% of the variance in PCA-level estimated emissions savings

182 Using APEX, we demonstrate that 73% of the variance in yeast protein abundance (47% in E. co

183 Genotype predicted a substantial 42-74% of the variance in receptor availability in women, depen

184 ative cis-regulatory element, explaining 74% of the variance in striatal Oxtr expression specifically

185 The thermodynamic model explains 75% of the variance in gene expression in synthetic promoter

186 factors accounted for 68% (95% CI, 60%-75%) of the variance in the susceptibility to psoriasis, for

187 etes mellitus, and for 74% (95% CI, 72%-76%) of the variance in BMI.

188 oduct of CAPE and precipitation explains 77% of the variance in the time series of total cloud-to-gro

189 explained 62% of the variance in FSS and 78% of the variance in ProF-S scores.

190 80% in functioning, while also capturing 79% of the variance in change in symptom severity (Quick Inv

191 hierarchical regression model explained 79% of the variance in change in GO-QOL appearance, with cha

192 model based on these data that explained 79% of the variance in the hiring of assistant professors an

193 at these three loci together explained 2.8 % of the variance in serum magnesium concentration in ARIC

194 The 14 loci accounted for an average of 1.8% of the variance in amino acid levels, which ranged from

195 We identified three loci that explained 2.8% of the variance in serum magnesium concentration in ARIC

196 espectively, and an additional 5.6% and 4.8% of the variance in SI and disposition index (P < 0.05),

197 way SNPs cumulatively explained 2.9% to 7.8% of the variance in ppFEV1 values in 4 populations (P = 3

198 constitutes the remaining approximately 80% of the variance in translation and explains approximatel

199 ed by temperature-related variables, and 81% of the variance in the mean family age of angiosperm tre

200 esponse in frontal regions accounted for 82% of the variance in the bilingual task-switching reaction

201 stment Scale), changes in IBI-D captured 83% of the variance in changes in QOL and 80% in functioning

202 The L4-L5 image explained 83% of the variance in VAT volume, and the covariates accoun

203 nd because POB(N) explained or predicted 83% of the variance in POB(I), it was considered a very good

204 rong, with genetic load explaining up to 83% of the variance in the drug response.

205 lity to psoriasis, for 73% (95% CI, 58%-83%) of the variance in susceptibility to type 2 diabetes mel

206 land, materials) together accounted for 84% of the variance in product rankings.

207 Approximately 85% of the variance in the mean family age of angiosperm tre

208 factors are important, determining up to 85% of the variance in some cone system response parameters.

209 of the standard lipid profile explained >86% of the variance in percentile discordance between TC/HDL

210 r with replication timing, explain up to 86% of the variance in mutation rates along cancer genomes.

211 e first principal component explaining 30.9% of the variance in SRS-2 scores, and a strong associatio

212 gen significantly explained an additional 9% of the variance in the lateral OFC volume (beta = -0.348

213 l, together explaining from approximately 9% of the variance in triglycerides, 5.8% of high-density l

214 ng visual search, accounting overall for 90% of the variance in human performance.

215 The model accounted for 90% of the variance in the experimental data.

216 FROH, FH and FE generally explained >90% of the variance in IBDG (among individuals) when 35 K or

217 -location-based planning predicts nearly 90% of the variance in novel movement sequences, even when m

218 of the patients combined explained over 90% of the variance in enlarging lesion volume over the subs

219 first four principal components covered 92% of the variance in product rankings, showing the potenti

220 gical measures, accounting for more than 92% of the variance in age.

221 ted for a median of 75.7% (IQR 45.8% to 92%) of the variance in methylation associated with ethnicity

222 ition and velocity sensitivity, captured 94% of the variance in the stimulus selectivity.

223 erence in nematocidal activity explained 94% of the variance in the data.

224 ch four dimensions capture approximately 95% of the variance in body shape.

225 A combined model explained 95% of the variance in HEV benefit for city, 75% for arteria

226 on identified 8 items that accounted for 95% of the variance in the full-scale PC-QOL questionnaire.

227 this unused protein expression explains >95% of the variance in growth rates of Escherichia coli acro

228 cated that the L4-L5 + 6 image explained 97% of the variance in total abdominal VAT volume, and addit

229 ethyl peaks (1.4-0.6 ppm) showed that 97.99% of the variance in the data is related to subject, 1.62%

230 bjects, accounting for a considerable amount of the variance in these measures.

231 ough directly predicting only a small amount of the variance in cannabis use, these findings suggest

232 d treatment beliefs explained a small amount of the variance in discordance in QOL.

233 lbirth but accounted for only a small amount of the variance in this outcome.

234 This validates further analysis of the variance in the transition region, which reveals

235 e environmental filtering is the main driver of the variance in functional composition.

236 r mixed model that provides robust estimates of the variance in responses to different stimuli.

237 al cognitive process that defines the extent of the variance in physical stimulus properties that bec

238 dually explained a relatively large fraction of the variance in tail morphology (a sexually dimorphic

239 Indeed, a large fraction of the variance in the expression can be explained by a

240 ndings, we found that a significant fraction of the variance in subjects' responses could be explaine

241 However a substantial fraction of the variance in overall outcome was not explained by

242 heterogeneity only explains a small fraction of the variances in longevity (5.9%), age at first repro

243 factors are estimated to explain about half of the variance in alcohol consumption, suggesting that

244 this method accounted for approximately half of the variance in long term cognitive and disability ou

245 hesis in the legumes, explaining nearly half of the variance in Asat .

246 re related to brain function, and up to half of the variance in age-related changes in cognition, bra

247 c factors have been reported to explain less of the variance in intelligence; the reverse is found fo

248 ll pad characteristics explained 14% or less of the variance in observed emission patterns, indicatin

249 s than genetic diversity, with only a little of the variance in mean d (2) among stranded seals expla

250 andomness parameter, a dimensionless measure of the variance in motor output.

251 Surprisingly, species explained far more of the variance in the isotopic niche during the non-bre

252 top-down control explains 7- to 10-fold more of the variance in abundance of bottom and mid-trophic l

253 cores] to be predictive of outcome, but most of the variance in functioning remains unexplained by su

254 from forestry and pasture) contributing most of the variance in estimated ILUC emissions intensity.

255 PSMD explained most of the variance in processing speed (R(2) ranging from 8

256 ntake of butter and margarine explained most of the variance in PUFA intake.

257 included 29 SNPs in 15 genes, explained most of the variance in the postprandial chylomicron lutein r

258 tions (i.e., synergies) can account for most of the variance in observed hand postures.

259 These EEG variables predicted most of the variance in inhibitory performance difference bet

260 aseline letter fluency scores predicted most of the variance in the drug's effect on cognitive contro

261 esentation of the data while preserving most of the variance in the data.

262 It is also shown that most of the variance in fitness contributed by new nonsynonym

263 However, we show that most of the variance in microbial time series is non-autoregr

264 was associated with, and accounted for much of the variance in, changes in negative and depressive s

265 However, much of the variance in neuronal activity remained unexplaine

266 We also demonstrate that much of the variance in the responses of anterior visual area

267 t genetic factors explain a substantial part of the variance in both NSSI (37% for men and 59% for wo

268 Eighty percent of the variance in average Hg concentrations in LMBE bet

269 Sixty-six percent of the variance in estimated VO(2max) could be accounted

270 l information, enabling a greater percentage of the variance in behaviour to be explained.

271 ity to TMS accounted for a modest percentage of the variance in the early after-effects of 1.0 mA ano

272 on models accounted for a smaller percentage of the variance in patients' intentions to ask doctors/n

273 e explained 80 (central) and 66 (peripheral) of the variance in pulse pressure in younger participant

274 ptor gene (DRD4) explains at least a portion of the variance in the traits.

275 ch explained only a small additional portion of the variance in peak oxygen consumption.

276 ion gain fields accounts for a large portion of the variance in the recorded data.

277 uniquely accounted for a significant portion of the variance in aggression over and above the effect

278 a test meal explained a significant portion of the variance in change in %BF in the overall group an

279 ubtype alone explained a significant portion of the variance in sensation (R(2) = 0.54, P < 0.001), w

280 osis subtype explained a significant portion of the variance in strength (R(2) = 0.30, P < 0.001).

281 plained overlapping and independent portions of the variance in working memory performance.

282 A large proportion of the variance in liability can be explained by shared

283 wind field--can replicate a large proportion of the variance in tropical Atlantic hurricane frequency

284 ortex accounted for a significant proportion of the variance in cognitive performance.

285 length (TL) explained a sizeable proportion of the variance in volume of the hippocampus, amygdala,

286 tic variant explains only a small proportion of the variance in brain microstructure, so we set out t

287 intake suggests that only a small proportion of the variance in REI was explained by change in feed i

288 nt images of faces, a substantial proportion of the variance in first impressions can be accounted fo

289 lity traits explain a substantial proportion of the variance in placebo analgesic responses and are f

290 The proportion of the variance in IL-1RA explained by both SNPs combine

291 The proportion of the variance in sepsis-related mortality explained by

292 ility for schizophrenia with about a quarter of the variance in liability to schizophrenia explained

293 rinogen) explained 20% and 3%, respectively, of the variance in fibrinogen gamma' and the gamma'/tota

294 plained 34.9%, 5.3%, and 4.5%, respectively, of the variance in changes in REE.

295 ug) accounted for 22% and 76%, respectively, of the variance in cPP.

296 The apparent similarity of the variance in recombination rate among individuals

297 variants, therefore, might account for some of the variance in ASE of APC.

298 nd interregional distance accounted for some of the variance in functional connectivity that was unex

299 erior segment explained only about one third of the variance in APAC occurrence, and the role of nona

300 and cognition explaining more than one-third of the variance in visual ability as measured by the AI.